Diagnostic value of H3F3A mutations in giant cell tumour of bone compared to osteoclast-rich mimics

Driver mutations in the two histone 3.3 (H3.3) genes, H3F3A and H3F3B, were recently identified by whole genome sequencing in 95% of chondroblastoma (CB) and by targeted gene sequencing in 92% of giant cell tumour of bone (GCT). Given the high prevalence of these driver mutations, it may be possible to utilise these alterations as diagnostic adjuncts in clinical practice. Here, we explored the spectrum of H3.3 mutations in a wide range and large number of bone tumours (n 5 412) to determine if these alterations could be used to distinguish GCT from other osteoclast-rich tumours such as aneurysmal bone cyst, nonossifying fibroma, giant cell granuloma, and osteoclast-rich malignant bone tumours and others. In addition, we explored the driver landscape of GCT through whole genome, exome and targeted sequencing (14 gene panel). We found that H3.3 mutations, namely mutations of glycine 34 in H3F3A, occur in 96% of GCT. We did not find additional driver mutations in GCT, including mutations in IDH1, IDH2, USP6, TP53. The genomes of GCT exhibited few somatic mutations, akin to the picture seen in CB. Overall our observations suggest that the presence of H3F3A p.Gly34 mutations does not entirely exclude malignancy in osteoclast-rich tumours. However, H3F3A p.Gly34 mutations appear to be an almost essential feature of GCT that will aid pathological evaluation of bone tumours, especially when confronted with small needle core biopsies. In the absence of H3F3A p.Gly34 mutations, a diagnosis of GCT should be made with caution.

Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large-scale human studies

Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such asMRI has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles to the use of MRI in large-scale studies. In this study we assess the validity of the recently proposed fat–muscle quantitation system (AMRATM Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images. Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using sliceOmatic, the current gold-standard, and the AMRATM Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by the two analysis methods, (Pearson correlation r = 0.97, p < 0.001), with the AMRATM Profiler analysis being significantly faster (~3 min) than the conventional sliceOmatic approach (~40 min). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 versus sliceOmatic 4.73 ± 1.75 l, p = 0.97). For the AMRATM Profiler analysis, the intra-observer coefficient of variation was 1.6% for IAAT and 1.1% for ASAT, the inter-observer coefficient of variationwas 1.4%for IAAT and 1.2%for ASAT, the intra-observer correlationwas 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRATM Profiler, opening up the possibility of large-scale human phenotypic studies.